Systematic Literature Review: Implementation of Machine Learning for Intrusion Detection - Amanda Amelia Khilda; M. Shaquille Rayhan; Annisa Rizki Amaliah; Nurbojatmiko Nurbojatmiko (Journal of Renewable Energy, Electrical, and Computer Engineering, 2025)
The rapid development of information technology has an impact on the increasing threat to cyber security. One of the main threats is intrusion attacks that are increasingly complex and diverse. To solve this problem, machine learning-based Intrusion Detection System (IDS) is a promising solution due to its ability to detect threats automatically and efficiently. However, the large number of machine learning methods available poses a challenge in determining the best approach for various needs. This research aims to conduct a systematic literature review using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. This literature review identifies and categorises previous studies related to the application of machine learning in IDSs based on the problem addressed, proposed solution, research method, metric parameters, research object, and research results. The data for this research is taken from trusted sources, such as Google Scholar, IEEE, Elsevier, Springer, and MDPI. The results of this review are expected to provide a deeper understanding of the application of machine learning in IDS and provide direction for other researchers to fill the remaining research gaps.